mari.py<\/p>\n\n\n\n
from flask import Flask, render_template, request
import random
import markovify
from transformers import GPT2LMHeadModel, GPT2Tokenizer<\/p>\n\n\n\n
app = Flask(name<\/strong>)<\/p>\n\n\n\n tokenizer = GPT2Tokenizer.from_pretrained(“gpt2”) text_data = []<\/p>\n\n\n\n def generate_gpt2_text(prompt, max_length=100): def build_markov_model(data): def generate_text_with_markov(model, num_sentences=3): @app.route(“\/”, methods=[“GET”, “POST”]) if name<\/strong> == “main<\/strong>“: templates<\/p>\n\n\n\n chat.html<\/p>\n\n\n\n \u30e6\u30fc\u30b6\u30fc: \u9001\u4fe1<\/p>\n\n\n\n {{ user_input }}<\/p>\n\n\n\n {{ gpt2_response }}<\/p>\n\n\n\n {{ markov_response }}<\/p>\n","protected":false},"excerpt":{"rendered":"GPT-2\u30e2\u30c7\u30eb\u306e\u30ed\u30fc\u30c9<\/h1>\n\n\n\n
model = GPT2LMHeadModel.from_pretrained(“gpt2”)<\/p>\n\n\n\n\u30de\u30eb\u30b3\u30d5\u9023\u9396\u7528\u306e\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u683c\u7d0d\u3059\u308b\u30ea\u30b9\u30c8<\/h1>\n\n\n\n
GPT-2\u306b\u3088\u308b\u81ea\u7531\u306a\u30c6\u30ad\u30b9\u30c8\u751f\u6210<\/h1>\n\n\n\n
input_ids = tokenizer.encode(prompt, return_tensors=”pt”)
output = model.generate(input_ids, max_length=max_length, num_return_sequences=1)
response = tokenizer.decode(output[0], skip_special_tokens=True)
return response<\/p>\n\n\n\n\u30de\u30eb\u30b3\u30d5\u9023\u9396\u30e2\u30c7\u30eb\u3092\u751f\u6210<\/h1>\n\n\n\n
text_model = markovify.NewlineText(data)
return text_model<\/p>\n\n\n\n\u30de\u30eb\u30b3\u30d5\u9023\u9396\u3092\u4f7f\u3063\u3066\u65b0\u3057\u3044\u30c6\u30ad\u30b9\u30c8\u3092\u751f\u6210<\/h1>\n\n\n\n
generated_text = model.make_sentence()
return generated_text<\/p>\n\n\n\n\u30a6\u30a7\u30d6\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u30eb\u30fc\u30c8<\/h1>\n\n\n\n
def chatbot():
user_input = “”
gpt2_response = “”
markov_response = “”<\/p>\n\n\n\nif request.method == \"POST\":\n user_input = request.form[\"user_input\"]\n\n # \u30e6\u30fc\u30b6\u30fc\u304b\u3089\u306e\u5165\u529b\u3092GPT-2\u306b\u9001\u4fe1\u3057\u3001\u751f\u6210\u3055\u308c\u305f\u30c6\u30ad\u30b9\u30c8\u3092\u53d6\u5f97\n gpt2_response = generate_gpt2_text(user_input)\n\n # GPT-2\u306e\u751f\u6210\u30c6\u30ad\u30b9\u30c8\u3092\u30ea\u30b9\u30c8\u306b\u8ffd\u52a0\n text_data.append(gpt2_response)\n\n # \u30de\u30eb\u30b3\u30d5\u9023\u9396\u30e2\u30c7\u30eb\u3092\u4f7f\u3063\u3066\u65b0\u3057\u3044\u30c6\u30ad\u30b9\u30c8\u3092\u751f\u6210\n markov_response = generate_text_with_markov(build_markov_model(text_data))\n\nreturn render_template(\"chat.html\", user_input=user_input, gpt2_response=gpt2_response, markov_response=markov_response)<\/code><\/pre>\n\n\n\n
app.run(debug=True)<\/p>\n\n\n\n
Chatbot<\/p>\n\n\n\nChatbot<\/h1>\n\n\n\n
\u30e6\u30fc\u30b6\u30fc\u306e\u5165\u529b:<\/h2>\n\n\n\n
GPT-2\u751f\u6210\u30c6\u30ad\u30b9\u30c8:<\/h2>\n\n\n\n
\u30de\u30eb\u30b3\u30d5\u9023\u9396\u751f\u6210\u30c6\u30ad\u30b9\u30c8:<\/h2>\n\n\n\n